Image super-resolution is one of the key problems of image restoration. While a lot of methods focus on working with known degradation (like bicubic downsampling), in real-world use cases the degradation models are complex and unknown and therefore difficult to prepare for. To train such a model in supervised manner paired data with real-world degradations is needed. As only unpaired high- and low-resolution data is usually available due to the high cost of real paired data collection and alignment, methods that tackle the blind super-resolution task face the problem of degradation choice for training data generation. Some of existing methods provide a degradation pipeline that include noise injection, jpeg compression, downsampling with different kernels, etc. These methods may be effective in some cases but offer no mechanism of the pipeline to adapt to real-world scenario therefore lacking the performance. The other approach presented in this paper is simulating the degradation directly without trying to construct it from a predefined list. This can be done using modern generative models such as diffusion models. These models has strong generalization capabilities and are known to simulate the data distributions well. The proposed method uses a diffusion model trained for low-resolution image generation to simulate the degradations and construct paired data given high-resolution data. We compare proposed diffusion-based method with the existing paired data generation techniques and show the performance boost for it.
This work is devoted to the research of applicability of depth determination methods for solving problems of building 3D models of rooms. The authors propose a combined method of finding the disparity and statistical signals processing using auxiliary data, obtained due to the laser illumination of the scene. Solutions used in forming 3D models of objects or the surrounding space are considered, which led to the definition of the most appropriate method for building a scanning system – the stereo-reconstruction method. The finding of disparity by naive gradient descent method is presented. The results of the scanning system are presented.
Stereo matching is one of the most important computer vision tasks. Several methods can be used to compute a matching cost of two pictures. This paper proposes a method that uses convolutional neural networks to compute the matching cost. The network architecture is described as well as teaching process. The matching cost metric based on the result of neural network is applied to base method which uses support points grid (ELAS). The proposed method was tested on Middlebury benchmark images and showed an accuracy improvement compared to the base method.
This paper proposes a stereo matching method that uses a support point grid in order to compute the prior disparity. Convolutional neural networks are used to compute the matching cost between pixels in two pictures. The network architecture is described as well as teaching process. The method was evaluated on Middlebury benchmark images. The results of accuracy estimation in case of using data from a LIDAR as an input for the support points grid is described. This approach can be used in multi-sensor devices and can give an advantage in accuracy up to 15%.
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