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In recent years, deep learning-based methods have emerged as dominant players in the field of super-resolution (SR), owing to their exceptional reconstruction performance. The primary driver of their effectiveness lies in their utilization of extensive sets of paired low-resolution and high-resolution images for training deep learning models. This training enables the models to effectively replicate the intricate mapping relationship between low-resolution and high-resolution images. Nevertheless, at present, acquiring a sufficient quantity of such image pairs that satisfy the requirements remains a formidable obstacle. Therefore, in order to break the restriction of limited training sets, self-supervised learning has been introduced to train a model for each low-quality image, without requiring pairwise ground-truths. However, they generally presuppose the generation of low-resolution (LR) images from their high-resolution (HR) counterparts using a pre-defined kernel, such as Bicubic downscaling. Such an assumption is seldom valid for real-world LR images, where degradation processes in practical applications are diverse, intricate, and often undisclosed. Therefore, when the presumed downscaling kernel does not match the actual one, the outcomes of state-of-the-art approaches degrade substantially. In this paper, we introduce KGSR, a kernel-guided network for addressing real-world blind SR, effectively avoiding requiring large training image pairs and transforming the blind image super-resolution problem into a supervised learning and non-blind scenario. Specifically, KGSR trains two networks, namely Upscaling and Downscaling, utilizing only patches extracted from the input test image. On one hand, owing to the cross-scale recurrence property of the SR kernel within a single image, the Downscaling network acquires knowledge of the image-specific degradation process through a generative adversarial network. Consequently, the Downscaling network is capable of generating a downsampled version of the LR test image even when the acquisition process is unknown or less than ideal. Additionally, we employ a dedicated discriminator to compel the Downscaling network to prioritize the characterization of kernel orientations. Conversely, a precise blur kernel has the potential to yield superior performance. Guided by the accurate image-specific SR kernel acquired from the Downscaling network and the downsampled LR input, the Upscaling network is capable of producing a high-quality HR image from the LR input. Within the Upscaling network, we additionally introduce an effective module for harnessing the acquired image-specific SR kernel. KGSR operates as a fully unsupervised approach, yet it can concurrently produce both the image-specific SR kernel and high-quality HR images. Comprehensive experiments conducted on standard benchmarks validate the efficacy of the proposed approach compared to state-of-the-art methodologies. Moreover, the suggested method can deliver visually appealing SR outcomes while exhibiting shorter processing times when applied to real-world LR images. • We iteratively refine features of HR images using a kernel. • A specific discriminator is introduced. • We use an explicit SR-kernel to produce high-quality HR images. • A feature transform module is employed in the network. [ABSTRACT FROM AUTHOR]

• A two-branch component divided guidance model is proposed for non-uniform deblurring. • An attention module adaptively divides the blur into large and small blurry regions. • An orientation-based fusion module to merge the two branches' de-blurred features. • Considering the guidance delivers a remarkable performance. • The parameters and computation of the model are smaller than other methods. Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists a major challenge with various non-uniform motion blur. Previous methods simply take all the image features as the input to the decoder, which handles different degrees (e.g. large blur, small blur) simultaneously, leading to challenges for sharp image generation. To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree. Specifically, two component attentive blocks are employed to learn attention maps to exploit useful deblurring feature representations on both large and small blurry regions. Then, the blur-aware features are fed into two-branch reconstruction decoders respectively. In addition, a new feature fusion mechanism, orientation-based feature fusion, is proposed to merge sharp features of the two branches. Both qualitative and quantitative experimental results show that our method performs favorably against the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]