<p>Deep learning (DL) has ushered in a suite of promising tools for image processing, including denoising (DN), deblurring (DB), and super-resolution (SR). However, traditional DL methods assume independent and identically distributed (i.i.d.) data for model training and inference, which does not hold in practice due to factors such as sample variation, variability in imaging conditions, and temporal effects in living cells. This discrepancy prevents models, even when trained on extensive datasets, from reaching their full performance potential during inference. This practical issue, unfortunately, is still unexplored. To address this issue, drawing inspiration from the way biological intelligence adapts through past experiences to shape future learning, we introduce HitchLearning—a revolutionary paradigm that breaks away from the conventional separation of training and inference. In HitchLearning, we leverage a single inference image to unsupervisedly optimize the model by aligning the training images with it. Subsequently, we employ this optimized model for processing individual inference images. This approach allows the model to adapt to the specific characteristics of each inference image, leading to improved results in a manner reminiscent of a “free lunch.” We conducted a thorough evaluation of our method across three distinct tasks within both supervised and unsupervised frameworks, utilizing four diverse datasets. Compared to conventional training methods, HitchLearning demonstrated average performance increases of 4.34 dB, 4.08 dB, and 0.54 dB for the DN, DN, and SR tasks, respectively. The experimental results unequivocally demonstrate that our algorithm offers a universally applicable and cost-free optimization solution for processing image and can be used in other fields as well.</p>