![]() ![]() Second, the needed texture information is variable for different tasks. Therefore, it is not proper to use VGG features as the texture features. First, the training objective of VGG network is a semantic classification label, and the high-level information is different from the low-level texture information. However, such design has obvious drawbacks. For texture extraction, recent approaches usually use semantic features extracted by a pre-trained classification model like VGG. Details will be discussed below.įigure 1: The proposed texture transformer. ![]() As shown in Figure 1, there are four parts in the texture transformer: the learnable texture extractor (LTE), the relevance embedding module (RE), the hard-attention module for feature transfer (HA) and the soft-attention module for feature synthesis (SA). Researchers at Microsoft Research Asia propose a novel texture transformer for image super-resolution to successfully apply transformer in image generation tasks. However, transformer is rarely used in image generation tasks. Transformer is widely used in natural language processing, which has achieved remarkable results. This approach can effectively search and transfer high-resolution texture information for the low-resolution input, which makes full use of the reference image to solve blurry effects and artifacts. We propose a novel Texture Transformer Network for Image Super-Resolution (TTSR). The introduction of a high-resolution reference image transforms the difficult texture generation process to a simple texture search and transfer, which achieves significant improvement in visual quality. RefSR approaches utilize information from a high-resolution image, which is similar to the input image, to assist in the recovery process. ![]() To address the above problems, reference-based image super-resolution (RefSR) is proposed as a new direction in the image SR field. Recently, some approaches adopt generative adversarial networks (GANs) to relieve the above problems, but the resultant hallucinations and artifacts caused by GANs further pose grand challenges to image SR tasks. Models trained with pixel-wise reconstruction loss functions often result in blurry effects for complex textures in the generated high-resolution results, which is far from satisfactory. Traditional single image super-resolution usually trains a deep convolutional neural network to recover a high-resolution image from the low-resolution image. Image super-resolution (SR) aims to recover natural and realistic textures for a high-resolution image from its degraded low-resolution counterpart, which is an important problem in the image enhancement field. Learning texture transformer network for image super-resolution These two works enable users to enhance their photos with ease, and the techniques were presented at CVPR 2020 (Computer Vision and Pattern Recognition). The proposed technique revives the photos to a modern form. To solve this, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. The second technique restores old photographs, which contain a mix of degradations that are hard to model. Compared to traditional learning-based methods, the reference-based solution solves the ambiguity of computer hallucination and achieves impressive visual quality. The first technique enhances the image resolution of an image file by referring to external reference images. In this blog, we are going to present our latest efforts in image enhancement. It is of tremendous benefit to save those degraded images so that users can reuse them for their own design or other aesthetic purposes. However, not all the images are captured by high-end DSLR cameras, and very often they suffer from imperfections. The amount of visual data we accumulate around the world is mind boggling. ![]()
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