SISR

In the Single Image Super Resolution with various prediction networks post, we used DIV2K and Flicker2K dataset for training. Below is a python snippet for both datasets. from glob import glob import torch from torch.utils.data import Dataset import torchvision from PIL import Image import numpy as np class SuperResolutionDataset(Dataset): def __init__(self, dataset_path, dataset_name_HR, dataset_name_LR, scale, name): self.name = name self.dataset_path = dataset_path self.dataset_name_HR = dataset_name_HR self.dataset_name_LR = dataset_name_LR self.scale = scale self....

The goal of single-image super-resolution (SISR) is to transform a low-resolution image into a high-resolution version, enhancing both its detail and quality. This process, called upscaling, increases the image’s spatial resolution while sharpening and clarifying its visual features. While the objective remains the same, the methods to achieve SISR can vary. In this post (Part 1), I’ll explore three different neural network-based SISR approaches: color prediction, residual prediction, and kernel prediction....