What to expect at MWC 2026: Best phones I'm anticipating from Xiaomi, Motorola, and more

Introduction

Mobile World Congress 2026 is approaching, and manufacturers like Xiaomi, Motorola, and others are already teasing exciting new smartphone technologies. In this tutorial, you'll learn how to work with the latest smartphone camera APIs and image processing techniques that these companies are likely to showcase. We'll build a practical image enhancement tool that demonstrates the kind of advanced camera processing we can expect from next-generation smartphones.

Prerequisites

Before beginning this tutorial, you'll need:

• Python 3.8 or higher installed on your system
• Basic understanding of Python programming
• Knowledge of image processing concepts
• Access to a computer with internet connection

Step-by-Step Instructions

1. Set up Your Development Environment

First, we need to create a clean development environment with the necessary libraries. This step ensures we have all the tools required for image processing and enhancement.

pip install opencv-python numpy pillow matplotlib

Why this step: These libraries provide the foundation for image manipulation, computer vision, and visualization that modern smartphones use for advanced camera features.

2. Create the Main Image Processing Class

Next, we'll create a class that will handle our image enhancement operations. This simulates how smartphone manufacturers implement advanced camera algorithms.

import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

class SmartphoneCameraEnhancer:
    def __init__(self):
        self.image = None
        
    def load_image(self, image_path):
        self.image = cv2.imread(image_path)
        if self.image is None:
            raise ValueError("Could not load image")
        
    def apply_hdr_processing(self):
        # Simulate HDR processing that smartphones use
        # This mimics the advanced algorithms we expect to see at MWC 2026
        gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
        enhanced = clahe.apply(gray)
        return enhanced
        
    def apply_noise_reduction(self):
        # Apply noise reduction similar to what we'll see in future smartphones
        denoised = cv2.fastNlMeansDenoisingColored(self.image, None, 10, 10, 7, 21)
        return denoised
        
    def apply_digital_zoom(self, zoom_factor=2.0):
        # Simulate digital zoom capabilities
        height, width = self.image.shape[:2]
        new_width = int(width / zoom_factor)
        new_height = int(height / zoom_factor)
        
        # Crop the center portion
        start_x = (width - new_width) // 2
        start_y = (height - new_height) // 2
        cropped = self.image[start_y:start_y+new_height, start_x:start_x+new_width]
        
        # Resize back to original dimensions
        zoomed = cv2.resize(cropped, (width, height), interpolation=cv2.INTER_LINEAR)
        return zoomed

Why this step: This class structure mirrors how smartphone manufacturers organize their camera processing pipelines, with separate methods for different enhancement techniques.

3. Implement Advanced Camera Features

Now we'll add more sophisticated features that we can expect to see in next-generation smartphones, such as computational photography enhancements.

    def apply_ai_enhancement(self):
        # Simulate AI-powered image enhancement
        # This represents the kind of machine learning capabilities we'll see
        # in smartphones from companies like Xiaomi and Motorola
        
        # Convert to LAB color space for better enhancement
        lab = cv2.cvtColor(self.image, cv2.COLOR_BGR2LAB)
        l, a, b = cv2.split(lab)
        
        # Apply CLAHE to L channel
        clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
        l_enhanced = clahe.apply(l)
        
        # Merge channels back
        lab_enhanced = cv2.merge([l_enhanced, a, b])
        
        # Convert back to BGR
        enhanced = cv2.cvtColor(lab_enhanced, cv2.COLOR_LAB2BGR)
        return enhanced
        
    def apply_low_light_boost(self):
        # Simulate low-light photography improvements
        # Modern smartphones use advanced sensor technology and computational algorithms
        
        # Increase brightness and contrast
        brightened = cv2.convertScaleAbs(self.image, alpha=1.2, beta=20)
        
        # Apply noise reduction
        denoised = cv2.fastNlMeansDenoisingColored(brightened, None, 15, 15, 7, 21)
        return denoised
        
    def save_result(self, output_path):
        if self.image is not None:
            cv2.imwrite(output_path, self.image)
            print(f"Enhanced image saved to {output_path}")

Why this step: These methods represent the kind of advanced computational photography features that will be showcased at MWC 2026, including AI enhancements and low-light processing.

4. Create a Complete Processing Pipeline

Now we'll create a complete workflow that demonstrates how these enhancements work together in a smartphone camera system.

def process_image_with_smartphone_features(input_path, output_path):
    # Create enhancer instance
    enhancer = SmartphoneCameraEnhancer()
    
    # Load image
    enhancer.load_image(input_path)
    
    # Apply sequential enhancements
    print("Applying HDR processing...")
    enhanced = enhancer.apply_hdr_processing()
    
    print("Applying noise reduction...")
    enhanced = enhancer.apply_noise_reduction()
    
    print("Applying AI enhancement...")
    enhanced = enhancer.apply_ai_enhancement()
    
    print("Applying low light boost...")
    enhanced = enhancer.apply_low_light_boost()
    
    # Save result
    enhancer.image = enhanced
    enhancer.save_result(output_path)
    
    return enhanced

Why this step: This pipeline demonstrates how modern smartphones apply multiple enhancement techniques in sequence, similar to how manufacturers like Samsung and Motorola will showcase their camera technologies.

5. Test Your Implementation

Finally, let's test our implementation with a sample image to see how it works.

# Create a sample image for testing
import cv2
import numpy as np

def create_sample_image():
    # Create a sample image with various lighting conditions
    img = np.zeros((400, 600, 3), dtype=np.uint8)
    
    # Add some pattern
    cv2.rectangle(img, (50, 50), (200, 200), (100, 150, 200), -1)
    cv2.rectangle(img, (300, 100), (500, 300), (200, 100, 150), -1)
    
    # Add some noise to simulate real conditions
    noise = np.random.randint(0, 50, img.shape, dtype=np.uint8)
    img = cv2.add(img, noise)
    
    cv2.imwrite('sample_test.jpg', img)
    return 'sample_test.jpg'

# Test the implementation
if __name__ == "__main__":
    # Create sample image
    sample_path = create_sample_image()
    
    # Process the image
    result = process_image_with_smartphone_features(sample_path, 'enhanced_output.jpg')
    
    # Display results
    original = cv2.imread(sample_path)
    plt.figure(figsize=(12, 6))
    plt.subplot(1, 2, 1)
    plt.imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
    plt.title('Original Image')
    plt.axis('off')
    
    plt.subplot(1, 2, 2)
    plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
    plt.title('Enhanced Image')
    plt.axis('off')
    
    plt.tight_layout()
    plt.show()

Why this step: Testing with a sample image helps validate that our implementation works correctly and demonstrates the enhancement capabilities that we can expect from next-generation smartphones.

Summary

In this tutorial, we've built a practical image enhancement tool that demonstrates the advanced camera technologies we can expect to see at Mobile World Congress 2026. We've implemented features like HDR processing, noise reduction, AI enhancement, and low-light boosting that smartphone manufacturers like Xiaomi, Motorola, and Samsung are likely to showcase. This hands-on approach gives you insight into how modern smartphones process images and prepares you for understanding the next generation of mobile camera technology.

By working through these steps, you've gained practical experience with image processing techniques that form the foundation of modern smartphone cameras, including computational photography algorithms and advanced enhancement methods that will be featured at upcoming technology conferences.