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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]