Vergleich_Upscaling_Methoden:
  Traditionelle_Interpolation:
    - Bikubische Interpolation
    - Lanczos-Resampling
    - Bilineare Interpolation
    Ergebnis: "Weichgezeichnet und unscharf"
    Qualität: "2/10 bei 4x Vergrößerung"

  KI_Super_Resolution:
    - ESRGAN (Enhanced Super-Resolution GAN)
    - Real-ESRGAN (Realistic ESRGAN)
    - EDSR (Enhanced Deep Super-Resolution)
    - SwinIR (Swin Transformer for Image Restoration)
    Ergebnis: "Scharfe Details, natürliche Texturen"
    Qualität: "8-9/10 bei 4x Vergrößerung"

# Real-ESRGAN Implementation
import cv2
import numpy as np
from realesrgan import RealESRGANer
from basicsr.archs.rrdbnet_arch import RRDBNet

class RealESRGANUpscaler:
    def __init__(self, model_name='RealESRGAN_x4plus'):
        """
        Real-ESRGAN Upscaler Initialisierung
        """
        # Modell-Konfiguration
        model_configs = {
            'RealESRGAN_x4plus': {
                'scale': 4,
                'model_path': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
                'netscale': 4,
                'num_block': 23,
                'num_feat': 64
            },
            'RealESRGAN_x2plus': {
                'scale': 2,
                'model_path': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
                'netscale': 2,
                'num_block': 23,
                'num_feat': 64
            },
            'RealESRNet_x4plus': {
                'scale': 4,
                'model_path': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth',
                'netscale': 4,
                'num_block': 23,
                'num_feat': 64
            }
        }

        config = model_configs[model_name]

        # Netzwerk-Architektur definieren
        self.model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=config['num_feat'],
            num_block=config['num_block'],
            num_grow_ch=32,
            scale=config['netscale']
        )

        # Upsampler initialisieren
        self.upsampler = RealESRGANer(
            scale=config['netscale'],
            model_path=config['model_path'],
            model=self.model,
            tile=0,  # 0 für automatische Tile-Größe
            tile_pad=10,
            pre_pad=0,
            half=False,  # FP16 für bessere Performance (GPU erforderlich)
            gpu_id=None  # CPU-Verwendung
        )

    def upscale_image(self, input_path, output_path, face_enhance=False):
        """
        Bild hochskalieren mit Real-ESRGAN
        """
        try:
            # Bild laden
            img = cv2.imread(input_path, cv2.IMREAD_COLOR)

            if img is None:
                raise ValueError(f"Konnte Bild nicht laden: {input_path}")

            print(f"Original Auflösung: {img.shape[1]}x{img.shape[0]}")

            # Upscaling durchführen
            output, _ = self.upsampler.enhance(img, outscale=4)

            print(f"Neue Auflösung: {output.shape[1]}x{output.shape[0]}")

            # Ergebnis speichern
            cv2.imwrite(output_path, output)

            return {
                'success': True,
                'input_resolution': f"{img.shape[1]}x{img.shape[0]}",
                'output_resolution': f"{output.shape[1]}x{output.shape[0]}",
                'upscale_factor': f"{output.shape[1] // img.shape[1]}x",
                'output_path': output_path
            }

        except Exception as e:
            return {
                'success': False,
                'error': str(e)
            }

    def batch_upscale(self, input_folder, output_folder, supported_formats=None):
        """
        Batch-Upscaling für mehrere Bilder
        """
        import os
        from pathlib import Path

        if supported_formats is None:
            supported_formats = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp']

        results = []
        input_path = Path(input_folder)
        output_path = Path(output_folder)

        # Output-Ordner erstellen
        output_path.mkdir(parents=True, exist_ok=True)

        # Alle Bilder im Input-Ordner verarbeiten
        for img_file in input_path.iterdir():
            if img_file.suffix.lower() in supported_formats:
                output_file = output_path / f"{img_file.stem}_upscaled{img_file.suffix}"

                print(f"Verarbeite: {img_file.name}")
                result = self.upscale_image(str(img_file), str(output_file))
                result['filename'] = img_file.name
                results.append(result)

        return results

# Praktische Verwendung
upscaler = RealESRGANUpscaler('RealESRGAN_x4plus')

# Einzelnes Bild upscalen
result = upscaler.upscale_image(
    input_path='low_res_image.jpg',
    output_path='high_res_image.jpg'
)

print(f"Upscaling erfolgreich: {result['success']}")
print(f"Von {result['input_resolution']} auf {result['output_resolution']}")

# ESRGAN für Anime und Kunstwerke
class AnimeESRGANUpscaler:
    def __init__(self):
        """
        Spezialisierter Upscaler für Anime/Manga/Artwork
        """
        self.anime_models = {
            'anime_4x': 'https://github.com/xinntao/ESRGAN/blob/master/models/RRDB_ESRGAN_x4.pth',
            'anime_2x': 'https://github.com/xinntao/ESRGAN/blob/master/models/RRDB_ESRGAN_x2.pth'
        }

    def upscale_anime_image(self, input_path, output_path, model='anime_4x'):
        """
        Spezialisiertes Upscaling für Anime-Bilder
        """
        # Anime-optimierte Verarbeitung
        processing_params = {
            'preserve_lines': True,  # Klare Linien erhalten
            'enhance_colors': True,  # Farben verstärken
            'reduce_noise': True,    # Kompressionsartefakte reduzieren
            'sharpen_details': True  # Details schärfen
        }

        return self.process_with_anime_optimization(input_path, output_path, processing_params)

# E-Commerce Bildoptimierung
class ECommerceImageUpscaler:
    def __init__(self):
        self.upscaler = RealESRGANUpscaler()
        self.quality_checker = ImageQualityChecker()

    def optimize_product_images(self, product_images_folder):
        """
        Optimierung von Produktbildern für E-Commerce
        """
        optimization_results = []

        for image_path in self.scan_product_images(product_images_folder):
            # Bildqualität bewerten
            quality_assessment = self.quality_checker.assess_image(image_path)

            optimization_plan = {
                'original_path': image_path,
                'needs_upscaling': quality_assessment['resolution'] < 1024,
                'needs_enhancement': quality_assessment['sharpness'] < 0.7,
                'compression_artifacts': quality_assessment['artifacts'] > 0.3
            }

            if optimization_plan['needs_upscaling']:
                # Upscaling durchführen
                upscaled_path = self.generate_upscaled_path(image_path)
                upscale_result = self.upscaler.upscale_image(image_path, upscaled_path)

                optimization_plan['upscaled_path'] = upscaled_path
                optimization_plan['upscale_success'] = upscale_result['success']

                # Verschiedene Größen für responsives Design generieren
                if upscale_result['success']:
                    responsive_sizes = self.generate_responsive_sizes(
                        upscaled_path,
                        sizes=[400, 800, 1200, 1600]
                    )
                    optimization_plan['responsive_images'] = responsive_sizes

            optimization_results.append(optimization_plan)

        return optimization_results

    def generate_responsive_sizes(self, source_image, sizes):
        """
        Generierung verschiedener Bildgrößen für responsives Design
        """
        import cv2

        responsive_images = {}
        source_img = cv2.imread(source_image)

        for size in sizes:
            # Proportionales Resizing
            height, width = source_img.shape[:2]
            if width > height:
                new_width = size
                new_height = int(height * (size / width))
            else:
                new_height = size
                new_width = int(width * (size / height))

            resized = cv2.resize(source_img, (new_width, new_height), interpolation=cv2.INTER_LANCZOS4)

            output_path = source_image.replace('.jpg', f'_{size}w.jpg')
            cv2.imwrite(output_path, resized, [cv2.IMWRITE_JPEG_QUALITY, 90])

            responsive_images[f'{size}w'] = output_path

        return responsive_images

# Historische Dokumente und Fotos restaurieren
class ArchiveImageRestoration:
    def __init__(self):
        self.upscaler = RealESRGANUpscaler()
        self.denoiser = ImageDenoiser()
        self.color_corrector = ColorCorrection()

    def restore_historical_image(self, image_path, restoration_level='standard'):
        """
        Umfassende Restaurierung historischer Bilder
        """
        restoration_pipeline = {
            'basic': ['upscale', 'denoise'],
            'standard': ['upscale', 'denoise', 'color_correction'],
            'advanced': ['upscale', 'denoise', 'color_correction', 'scratch_removal', 'contrast_enhancement']
        }

        steps = restoration_pipeline[restoration_level]
        current_image = image_path
        results = {'original': image_path}

        for step in steps:
            if step == 'upscale':
                upscaled_path = self.generate_temp_path(current_image, '_upscaled')
                upscale_result = self.upscaler.upscale_image(current_image, upscaled_path)
                if upscale_result['success']:
                    current_image = upscaled_path
                    results['upscaled'] = upscaled_path

            elif step == 'denoise':
                denoised_path = self.generate_temp_path(current_image, '_denoised')
                self.denoiser.remove_noise(current_image, denoised_path)
                current_image = denoised_path
                results['denoised'] = denoised_path

            elif step == 'color_correction':
                corrected_path = self.generate_temp_path(current_image, '_color_corrected')
                self.color_corrector.correct_colors(current_image, corrected_path)
                current_image = corrected_path
                results['color_corrected'] = corrected_path

        # Finales Ergebnis
        final_path = image_path.replace('.', '_restored.')
        self.copy_file(current_image, final_path)
        results['final'] = final_path

        return results

# Marketing-Materialien für Druck optimieren
class PrintQualityUpscaler:
    def __init__(self):
        self.upscaler = RealESRGANUpscaler()

    def prepare_for_print(self, image_path, print_format, dpi=300):
        """
        Bilder für Druckqualität vorbereiten
        """
        print_requirements = {
            'business_card': {'width_cm': 8.5, 'height_cm': 5.5},
            'flyer_a4': {'width_cm': 21, 'height_cm': 29.7},
            'poster_a3': {'width_cm': 29.7, 'height_cm': 42},
            'banner_xl': {'width_cm': 100, 'height_cm': 70}
        }

        requirements = print_requirements[print_format]

        # Benötigte Pixel-Auflösung berechnen
        required_width_px = int(requirements['width_cm'] * dpi / 2.54)
        required_height_px = int(requirements['height_cm'] * dpi / 2.54)

        print(f"Benötigte Auflösung für {print_format}: {required_width_px}x{required_height_px}")

        # Aktuelles Bild analysieren
        import cv2
        current_img = cv2.imread(image_path)
        current_height, current_width = current_img.shape[:2]

        # Upscaling-Faktor berechnen
        width_factor = required_width_px / current_width
        height_factor = required_height_px / current_height
        needed_factor = max(width_factor, height_factor)

        if needed_factor > 1:
            print(f"Upscaling um Faktor {needed_factor:.1f} erforderlich")

            # Mehrstufiges Upscaling für beste Qualität
            if needed_factor > 4:
                # Erste Stufe: 4x Upscaling
                temp_path = self.generate_temp_path(image_path, '_4x')
                result = self.upscaler.upscale_image(image_path, temp_path)

                # Zweite Stufe: Weitere Vergrößerung per Interpolation
                final_img = cv2.imread(temp_path)
                final_resized = cv2.resize(
                    final_img,
                    (required_width_px, required_height_px),
                    interpolation=cv2.INTER_LANCZOS4
                )
            else:
                # Einstufiges Upscaling
                upscaled_path = self.generate_temp_path(image_path, '_upscaled')
                self.upscaler.upscale_image(image_path, upscaled_path)

                upscaled_img = cv2.imread(upscaled_path)
                final_resized = cv2.resize(
                    upscaled_img,
                    (required_width_px, required_height_px),
                    interpolation=cv2.INTER_LANCZOS4
                )
        else:
            # Nur Resize erforderlich
            final_resized = cv2.resize(
                current_img,
                (required_width_px, required_height_px),
                interpolation=cv2.INTER_LANCZOS4
            )

        # Print-optimiertes Bild speichern
        output_path = image_path.replace('.', f'_print_{print_format}.')
        cv2.imwrite(output_path, final_resized, [cv2.IMWRITE_JPEG_QUALITY, 95])

        return {
            'output_path': output_path,
            'final_resolution': f"{required_width_px}x{required_height_px}",
            'dpi': dpi,
            'print_format': print_format,
            'upscaling_applied': needed_factor > 1
        }

Online_KI_Upscaling_Tools:
  Kostenlose_Optionen:
    - 'Waifu2x (speziell für Anime)'
    - 'Real-ESRGAN Online (verschiedene Anbieter)'
    - 'Upscayl (Desktop-App, kostenlos)'
    - 'AI Image Enlarger'

  Vorteile:
    - 'Keine Installation erforderlich'
    - 'Sofort verfügbar'
    - 'Meist kostenlos für kleine Bilder'

  Nachteile:
    - 'Datenschutz-Bedenken'
    - 'Größenlimitationen'
    - 'Wartezeiten bei hoher Nutzung'
    - 'Keine Batch-Verarbeitung'

Lokale_Lösungen:
  Desktop_Anwendungen:
    - 'Topaz Gigapixel AI (kommerziell)'
    - 'Real-ESRGAN (Open Source)'
    - 'ESRGAN (Open Source)'
    - 'Upscayl (kostenlose GUI)'

  Vorteile:
    - 'Vollständige Datenkontrolle'
    - 'Keine Größenlimitationen'
    - 'Batch-Verarbeitung'
    - 'Offline verfügbar'

  Nachteile:
    - 'GPU erforderlich für beste Performance'
    - 'Setup-Aufwand'
    - 'Speicherplatz für Modelle'

# Enterprise KI Upscaling Service
class EnterpriseUpscalingService:
    def __init__(self):
        self.api_endpoints = {
            'real_esrgan': 'https://api.your-company.com/upscale/real-esrgan',
            'esrgan': 'https://api.your-company.com/upscale/esrgan',
            'custom_model': 'https://api.your-company.com/upscale/custom'
        }
        self.batch_processor = BatchProcessor()
        self.quality_monitor = QualityMonitor()

    def enterprise_upscale(self, images, options):
        """
        Enterprise-Upscaling mit SLA und Qualitätsgarantien
        """
        processing_plan = {
            'total_images': len(images),
            'estimated_time': self.estimate_processing_time(images),
            'quality_tier': options.get('quality', 'standard'),
            'batch_size': options.get('batch_size', 10),
            'priority': options.get('priority', 'normal')
        }

        # Qualitäts-Monitoring
        results = []
        for batch in self.batch_processor.create_batches(images, processing_plan['batch_size']):
            batch_results = self.process_batch(batch, options)

            # Qualitätskontrolle
            for result in batch_results:
                quality_score = self.quality_monitor.assess_result(result)
                if quality_score < 0.8:
                    # Reprocessing mit anderen Parametern
                    result = self.reprocess_with_fallback(result)

                results.append(result)

        return {
            'processing_plan': processing_plan,
            'results': results,
            'quality_metrics': self.generate_quality_report(results),
            'sla_compliance': self.check_sla_compliance(processing_plan, results)
        }

# GPU-optimierte Verarbeitung
class GPUOptimizedUpscaler:
    def __init__(self, gpu_memory_gb=8):
        self.gpu_memory = gpu_memory_gb
        self.tile_size = self.calculate_optimal_tile_size()

    def calculate_optimal_tile_size(self):
        """
        Optimale Tile-Größe basierend auf verfügbarem GPU-Speicher
        """
        memory_requirements = {
            4: 256,   # 4GB GPU -> 256px Tiles
            6: 384,   # 6GB GPU -> 384px Tiles
            8: 512,   # 8GB GPU -> 512px Tiles
            12: 768,  # 12GB GPU -> 768px Tiles
            16: 1024  # 16GB+ GPU -> 1024px Tiles
        }

        for memory_threshold in sorted(memory_requirements.keys()):
            if self.gpu_memory <= memory_threshold:
                return memory_requirements[memory_threshold]

        return 1024  # Fallback für sehr große GPUs

    def optimize_batch_processing(self, images):
        """
        Batch-Größe für optimale GPU-Auslastung
        """
        # Durchschnittliche Bildgröße berechnen
        avg_pixels = sum(img.width * img.height for img in images) / len(images)

        # Batch-Größe basierend auf GPU-Speicher und Bildgröße
        if avg_pixels > 1920 * 1080:  # Große Bilder
            return 1
        elif avg_pixels > 1280 * 720:  # Mittlere Bilder
            return 2
        else:  # Kleine Bilder
            return min(4, self.gpu_memory // 2)

# Qualitätsbewertung für upscaled Images
class ImageQualityAssessment:
    def __init__(self):
        self.metrics_calculator = ImageMetricsCalculator()

    def assess_upscaling_quality(self, original_path, upscaled_path):
        """
        Umfassende Qualitätsbewertung des Upscaling-Ergebnisses
        """
        import cv2
        import numpy as np
        from skimage.metrics import structural_similarity as ssim
        from skimage.metrics import peak_signal_noise_ratio as psnr

        # Bilder laden
        original = cv2.imread(original_path)
        upscaled = cv2.imread(upscaled_path)

        # Original auf upscaled Größe skalieren für Vergleich
        original_resized = cv2.resize(
            original,
            (upscaled.shape[1], upscaled.shape[0]),
            interpolation=cv2.INTER_CUBIC
        )

        # Metriken berechnen
        quality_metrics = {
            'sharpness_score': self.calculate_sharpness(upscaled),
            'detail_preservation': self.calculate_detail_preservation(original, upscaled),
            'artifact_score': self.detect_artifacts(upscaled),
            'color_accuracy': self.assess_color_accuracy(original_resized, upscaled),
            'overall_quality': 0  # Wird am Ende berechnet
        }

        # Gewichtete Gesamtbewertung
        weights = {
            'sharpness_score': 0.3,
            'detail_preservation': 0.3,
            'artifact_score': 0.2,  # Niedrigere Artefakte = bessere Bewertung
            'color_accuracy': 0.2
        }

        overall_score = sum(
            quality_metrics[metric] * weight
            for metric, weight in weights.items()
            if metric != 'overall_quality'
        )

        # Artifact Score umkehren (weniger Artefakte = bessere Bewertung)
        overall_score += (1 - quality_metrics['artifact_score']) * weights['artifact_score']
        overall_score -= quality_metrics['artifact_score'] * weights['artifact_score']

        quality_metrics['overall_quality'] = overall_score

        return quality_metrics

    def calculate_sharpness(self, image):
        """
        Schärfe-Bewertung mittels Laplacian Variance
        """
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()

        # Normalisierung auf 0-1 Skala
        return min(laplacian_var / 1000, 1.0)

    def detect_artifacts(self, image):
        """
        Erkennung von Upscaling-Artefakten
        """
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        # Blockiness Detection
        blockiness = self.detect_blockiness(gray)

        # Ringing Detection
        ringing = self.detect_ringing(gray)

        # Noise Level
        noise_level = self.estimate_noise_level(gray)

        # Kombinierte Artefakt-Bewertung
        artifact_score = (blockiness + ringing + noise_level) / 3

        return artifact_score

    def generate_quality_report(self, quality_metrics):
        """
        Menschenlesbarer Qualitätsbericht
        """
        score_to_grade = {
            (0.9, 1.0): 'Ausgezeichnet',
            (0.8, 0.9): 'Sehr gut',
            (0.7, 0.8): 'Gut',
            (0.6, 0.7): 'Befriedigend',
            (0.5, 0.6): 'Ausreichend',
            (0.0, 0.5): 'Mangelhaft'
        }

        overall_score = quality_metrics['overall_quality']

        for score_range, grade in score_to_grade.items():
            if score_range[0] <= overall_score <= score_range[1]:
                quality_grade = grade
                break

        report = f"""
        Qualitätsbewertung Upscaling-Ergebnis:

        Gesamtbewertung: {overall_score:.2f} ({quality_grade})

        Detail-Metriken:
        - Schärfe: {quality_metrics['sharpness_score']:.2f}
        - Detail-Erhaltung: {quality_metrics['detail_preservation']:.2f}
        - Artefakte: {quality_metrics['artifact_score']:.2f} (niedriger = besser)
        - Farbgenauigkeit: {quality_metrics['color_accuracy']:.2f}

        Empfehlung: {'Ergebnis akzeptieren' if overall_score > 0.7 else 'Alternative Methode versuchen'}
        """

        return report

Preprocessing_Best_Practices:
  Bildvorbereitung:
    - 'Originaldatei in höchster verfügbarer Qualität verwenden'
    - 'Kompressionsartefakte vor Upscaling reduzieren'
    - 'Kontrast und Helligkeit optimieren'
    - 'Noise Reduction bei sehr verrauschten Bildern'

  Format_Auswahl:
    - 'PNG für verlustfreie Qualität'
    - 'TIFF für professionelle Workflows'
    - 'JPEG nur mit höchster Qualitätseinstellung'

  Auflösungs_Strategie:
    - 'Nicht mehr als 4x in einem Schritt upscalen'
    - 'Bei größeren Faktoren: Mehrstufiges Upscaling'
    - 'Custom Models für spezifische Bildtypen verwenden'

# Model-Auswahl basierend auf Bildtyp
class ModelSelectionGuide:
    def __init__(self):
        self.model_recommendations = {
            'photography': {
                'portraits': 'Real-ESRGAN_x4plus',
                'landscapes': 'Real-ESRGAN_x4plus',
                'street': 'Real-ESRGAN_x4plus',
                'macro': 'RealESRNet_x4plus'
            },
            'artwork': {
                'anime': 'Real-ESRGAN_x4plus_anime_6B',
                'manga': 'Real-ESRGAN_x4plus_anime_6B',
                'digital_art': 'Real-ESRGAN_x4plus',
                'traditional_art': 'Real-ESRGAN_x4plus'
            },
            'documents': {
                'text': 'RealESRNet_x4plus',
                'technical_drawings': 'RealESRNet_x4plus',
                'scanned_documents': 'RealESRNet_x4plus'
            },
            'commercial': {
                'product_photos': 'Real-ESRGAN_x4plus',
                'marketing_materials': 'Real-ESRGAN_x4plus',
                'logos': 'RealESRNet_x4plus'
            }
        }

    def recommend_model(self, image_analysis):
        """
        Empfehlung des optimalen Models basierend auf Bildanalyse
        """
        image_type = self.classify_image_type(image_analysis)
        subtype = self.classify_image_subtype(image_analysis, image_type)

        recommended_model = self.model_recommendations[image_type][subtype]

        return {
            'recommended_model': recommended_model,
            'confidence': image_analysis['classification_confidence'],
            'alternative_models': self.get_alternative_models(image_type),
            'reasoning': f"Optimal für {image_type}/{subtype}"
        }

KI_Upscaling_Kosten_Nutzen:
  Kostenlose_Lösungen:
    Setup_Aufwand: '2-4 Stunden'
    Laufende_Kosten: '€0 (nur Strom)'
    Hardware_Anforderungen: 'GPU empfohlen (GTX 1060+)'
    Qualität: '8-9/10'

  Kommerzielle_Software:
    Topaz_Gigapixel_AI: '€99 einmalig'
    Adobe_Camera_Raw: '€24/Monat (Creative Cloud)'
    Setup_Aufwand: '30 Minuten'
    Qualität: '9/10'

  Cloud_Services:
    AWS_SageMaker: '€0.50-2.00 pro Bild'
    Google_Cloud_AI: '€0.30-1.50 pro Bild'
    Azure_Cognitive: '€0.40-1.80 pro Bild'
    Skalierbarkeit: 'Unbegrenzt'

  ROI_Berechnung:
    Zeitersparnis: '90% vs. manuelle Bearbeitung'
    Qualitätsverbesserung: '300-500% vs. traditionelle Methoden'
    Kostenersparnis: '€50-200 pro Bild (vs. Profi-Bearbeitung)'